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Data Services Class Descriptions

Information, materials, and schedules for all currently offered Data Services classes.
Getting Started with Python Pandas is an intermediate-to-advanced level class that offers basic strategies for reading, cleaning, and visualizing data with the Pandas Python library.
Software: Python, Jupyter Notebooks, Pandas 

Computer workstations with Anaconda Python/Jupyter Notebook and Pandas installed are available for in-person tutorials in Bobst 617. For remote tutorials, while some patrons decide to approach tutorials as a demonstration of the software, other patrons approach tutorials with a more “hands-on” approach and wish to interact with the software during the tutorial. If the latter is the case, we recommend referencing our supported software page for additional information on accessing the software prior to the tutorial.
Duration: 120 min

Room description:

Some tutorials are held remotely and require NYU sign on to access, while others are held in person, without a remote component. Please note the correct modality and location of the tutorial when registering
Prerequisites:
  • Familiarity with core Python objects types (lists, dictionaries, strings, numbers, functions)
  • Familiarity with common data storage file types such as CSV
  • Comfort with using Jupyter Notebooks for writing code
  • Preferred familiarity with data table structures and concepts like sorting, filtering, merging, and having common table keys
Skills Taught / Learning Outcomes:
  • Understand the building blocks of a Pandas dataframe
  • Know how to make a dataframe and how to load it with data
  • Filtering, selecting, and other common operations needed to focus on a subset of a dataframe
  • Updating values
  • Table joins and merges
  • Exporting a dataframe to a saved file
Class Materials: https://github.com/NYU-DataServices/startingpandas
Related Classes:
  • Introduction to Python
  • Data Cleaning Using OpenRefine
  • Data Visualization with Tableau
  • Introduction to Jupyter Notebooks
  • Introduction to Research Data Management
Additional Training Materials:

Data analysis with Python and Pandas [video]
Feedback: bit.ly/feedbackds

 

Upcoming sessions for this tutorial

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